EAI Endorsed Transactions on e-Learning

The Analysis of How Artificial Intelligence Has an Effect on Teachers and The Education System

2023-06-27T18:23:33+00:00

Artificial intelligence has established a presence in every aspect of human activity, further demonstrating its preeminence over humans with each passing day. Artificial Intelligence (AI) has demonstrated its prowess in a variety of industries, including but not limited to healthcare, robotics, eCommerce, finance, navigation, education (E), and many more. This study will investigate the effects that artificial intelligence has had on the educational system, with a particular emphasis on how AI has changed the responsibilities that instructors play in the classroom. This study will also focus on examining whether or not the presence of AI in the educational system will take over the responsibilities that are traditionally filled by teachers. The incorporation of artificial intelligence into educational and instructional systems has resulted in significant improvements in terms of the efficiency, precision, and variety of pedagogical approaches. It has also been demonstrated in a large number of studies that the most important factor in the achievement of AI domination in educational institutions is the role played by educators.

The E-learning for Alzheimer's Disease

2023-10-28T15:20:59+00:00

With the increase of the aging population, the incidence rate of Alzheimer's disease (AD) is also rising. Faced with this challenge, e-learning, as an innovative educational method, has shown great potential in the care and management of Alzheimer's disease patients. This article reviews the application progress of E-learning in Alzheimer's disease. E-learning has revolutionized the field of education, providing learners with accessible and flexible learning opportunities. This paper provides an overview of various aspects of e-learning, including virtual classrooms, mobile learning, blended learning, Massive Open Online Courses (MOOCs), webinars, and the challenges associated with implementing e-learning.

The background section explores the evolution of e-learning, highlighting its rise in popularity and the advancements in technology that have facilitated its growth. Virtual classrooms for adult learners are discussed, showcasing how these online platforms facilitate interactive and collaborative learning experiences. Mobile learning for adult learners is examined, emphasizing the convenience and accessibility provided by mobile devices in delivering educational content.

Blended learning is another approach explored in this paper, which combines traditional face-to-face instruction with online components, offering a balanced learning experience. The benefits and challenges of implementing MOOCs, which provide free and open access to educational resources from top institutions, are also examined. Additionally, webinars are discussed as a popular method for delivering live online presentations and workshops to adult learners.

Finally, the paper addresses the challenges of E-learning, including technological barriers, lack of personal interaction, and the need for self-discipline and motivation. Strategies for overcoming these challenges are suggested, such as providing technical support and fostering online community engagement.

Overall, this paper provides valuable insights into the background and various approaches to E-learning, as well as the challenges encountered in its implementation. Understanding these aspects will help educators and institutions effectively harness the potential of E-learning to enhance adult education.

Microlearning helps Alzheimer’s Disease Patients

2023-11-07T11:14:26+00:00

Alzheimer's disease is one of the most common diseases in older adults, and as the disease progresses, the need for daily care increases. Caregivers of Alzheimer's Disease patients face a variety of stresses and work pressures. Receiving professional and continuous training is one of the effective ways to improve their skills and competencies. A new approach to education is microlearning, where microeducational content is provided to learners. Microlearning as a pedagogical technique focuses on designing learning modules through micro-steps in a digital media environment. These activities can be integrated into learners' daily lives and tasks. Unlike "traditional" e-learning methods, microlearning often favours technology delivered through push media, thus reducing the cognitive load on the learner. Microlearning educational methods have been shown to be effective and efficient in educating and delivering materials to caregivers of older adults with Alzheimer's disease. This paper begins with a brief introduction to microlearning. And it details the key features and benefits of microlearning. Microlearning offers potential benefits to Alzheimer's Disease patients and their caregivers with its concise and focused approach. Secondly, machine learning enhances the design and delivery of microlearning, helping to provide a more personalised and effective learning experience. Machine learning plays a role in the design of microlearning. To conclude, microlearning offers a promising avenue of support and care for Alzheimer's Disease patients. Microlearning also provides a valuable resource for carers and healthcare professionals to gain the knowledge and skills needed to provide effective care.

Use MOOC to learn image denoising techniques

2023-11-15T14:54:19+00:00

This article focuses on using MOOCs to learn image denoising techniques. It begins with an introduction to the concept of MOOCs - these innovative online learning platforms that offer a wide range of courses across disciplines, providing convenient and affordable learning opportunities for a global audience. It then explains the characteristics of MOOC's wide coverage, high flexibility, and different from traditional education models. It then introduces the advantages of MOOCs: accessibility and inclusiveness (open to anyone with an Internet connection), cost-effectiveness (a cost-effective alternative, many courses available for free), flexibility and self-paced learning (the ability to learn at your own pace), a diverse curriculum and global expertise. Then the concept of image denoising is introduced - image denoising is a basic process of digital image processing, and the common denoising methods are described: filter method and the applicable range of various filters, the advantages and disadvantages of wavelet change, the advantages of deep learning method and the principle of non-local mean denoising technology. It then describes how MOOCs can help learn image denoising: integrating course content, getting expert guidance, hands-on exercises and projects, and community and peer communication. In addition, it introduces the challenges encountered by MOOCs: high dropout rate, quality and credibility of MOOCs, lack of interaction and humanization in traditional classrooms, accessibility. The relationship between E-learning and MOOC is also introduced – E-learning and MOOC play complementary roles in modern education. MOOC provide a structured, flexible, cost-effective environment and a transformative educational experience for learning about biological image denoising.

Controllable Privacy-Preserving Online Diagnosis with Outsourced SVM over Encrypted Medical Data

2023-11-18T10:18:22+00:00

With the widespread application of online diagnosis systems, users can upload their physical characteristics anytime and from anywhere to receive clinical diagnoses. However, for privacy and intellectual property considerations, users' physical characteristics, diagnosis results, and the medical diagnosis model should be protected. To achieve an efficient and secure online diagnosis, secure outsourcing and low burden become research objectives. However, few of the existing privacy-preserving schemes focus on the secure outsourcing of the training process, and few consider the supervision of the hospital for the online diagnosis process. By introducing a four-party architecture with two non-colluding servers, a hospital and users, in this paper, we propose a controllable privacy-preserving online diagnosis scheme (CPPOD) with outsourced SVM over encrypted medical data. Concretely, an integer vector homomorphic encryption is employed to protect medical data and user requests. In the encrypted domain, a series of collaborative protocols including data collection, sequence minimum optimization solver, SVM model building, and online diagnosis are constructed and take place between different participants, while no significant increase in computation on either the hospital or user side. CPPOD enables the hospital to delegate online diagnosis services to a cloud server while ensuring that its regulatory capabilities cannot be bypassed unauthorized. Security analysis and performance evaluation suggest that CPPOD performs well regarding security and efficiency.

Development and Perceived Usability Evaluation of a Mobile application for Notetaking

2023-12-04T13:08:23+00:00

INTRODUCTION: Notetaking is considered, by many educators, as one of the critical actions of learning. There are several note-taking methods and approaches. Based on these methods and approaches, various applications, whether mobile, desktop or -Web-based, were developed.

OBJECTIVES: In this paper, a novel note-taking application based on Cornell Technique, is presented. Its development process and user acceptance trend are exhibited and results for user evaluation based on user satisfaction are presented.

METHODS: For the software development process, Incremental Model was adopted. Requirement Analysis included, aside from examining principles and related note-taking structure of Cornell Technique, investigating (i) how to perform notetaking as an activity of learning, (ii) its product and (iii) relationship of notes for the purpose of storage. Models containing sub-activities, such as reviewing note have been identified and some were selectively adopted and related functions such as review alert (tickler) and collaboration on notetaking have been implemented. To the purpose of storage, a tree-based scheme called collection was modelled. User interfaces were first designed as mockups and click-through pro-totype using Adobe XD. The mobile application was implemented in Dart programming language. Google’s Firebase Service and Flutter Framework was adopted. The mobile application was compared with its equivalents in the Google Play Store and user statistics were investigated. To evaluate perceived usability, the System Usability Scale is adopted and applied to 14 university students conforming to determined persona.

RESULTS: The application has been published in Google Play Store for users to install for free on 18^th March 2022. As of 10^th September 2023, total number of downloads is 5K and the Cornell Note mobile app is currently installed on 1108 devices. For the last three-month period (from 11^th June to 10^th September 2023), the active users per month changed in an increasing trend from 450 to 589. The average engagement time on 11^th of April 2023 was 28 minutes 00 seconds. As the number of monthly active users increased, the average engagement time measured on 10^th September 2023 decreased to 23 minutes 31 seconds. However, engagement rates measured were 76.91% and 77.19%, respectively. The mean SUS score was found to be equal to 79.5.

CONCLUSION: The user statistics and comparison with equivalent mobile applications reveal that Cornell Note has potential to grow as a mobile application for notetaking since it has a good perceived usability, however, there is room for improvement. Considering any extra marketing effort was not spent for the application such as application store optimization, the statistics are another evidence for user appeal and acceptance. However, it is important to add new functionality without complicating the user experience so that user appeal and acceptance boosts.

Review of Graph Neural Networks for Medical Image

2023-11-10T10:49:27+00:00

As deep learning continues to evolve, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. Because, due to the uniqueness of graphs, applying deep learning to ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, graph Neural Networks (GNNs) have emerged. A graph neural network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. With the continuous development of medical image technology, medical image diagnosis plays a crucial role in clinical practice. However, in practice, medical images are often affected by noise, artifacts, and other interfering factors, which may lead to inaccurate and unstable diagnostic results. Therefore, image-denoising techniques become especially critical in medical image processing. Therefore, researchers have proposed innovative methods based on graph neural networks for effective noise removal, preserving the key features of the image and improving the quality and usability of medical images. This paper reviews the research progress of graph neural networks in the field of medical image denoising. It also summarises the problems and challenges of current research and looks at the future direction of medical image-denoising research.